Training shows census-based method linking Lamont water contamination to racial exposure patterns

An online training hosted for water-board staff and partners demonstrated a reproducible, census-based method to identify racial disparities in exposure to a failing drinking-water system in Lamont, Kern County.

Instructor Hannah Cushman Garland, partner at DataMaid, led the session and used the Lamont case to show how decennial census block data, a public dataset that predicts private-well versus public-water use, and dot-density mapping can be combined to measure who is likely to be exposed. The exercise found that while Lamont’s total population is majority Hispanic, Hispanic residents were both numerically dominant and disproportionately likely to depend on the public water system that contained exceedances of maximum contaminant levels for arsenic and 1,2,3-trichloropropane.

The training taught a reproducible workflow: obtain a Census API key; pull 2020 decennial block-level counts with the tidycensus package; pivot and aggregate to the desired geography; fetch an EPA-derived dataset that estimates the probability a block relies on public water; join datasets by census block; and produce both choropleth and dot-density maps in R (using ggplot2 and a dots package). Garland said the working definition of racial equity for the module was…